• I.Sarfaraz Ahamed
• S.Vivek
• Dr.A.Balamurugan

Graph mining is a significant field which has it applications in various fields such as computer and social networks, chemical industry and in bioinformatics. Variety of mining techniques are implemented in a graph to mine the useful information. The graph mining framework has been proposed in which all the frequent tree patterns from a graph database are mined initially and then the maximal frequent subgraphs from trees are constructed. The proposed method allows the user to assimilate well-develop techniques from maximal itemsets and information gained in graph mining into a new minimum spanning tree algorithm. A novel maximal spanning tree-based graph mining algorithm is proposed to mine only maximal frequent subgraphs from a large graph database. An extensive analysis of the proposed algorithm has been made to analyze its performance on graph data sets with different characteristics. The efficiency of the algorithm is also confirmed by two benchmark datasets. The experimental result shows the better performance of the proposed method in comparison with the existing graph mining algorithm.

Graph Mining, frequent pattern mining, maximal itemset, maximal frequent subgraph, spanning tree

"PRIVATE MAXIMAL FREQUENT DIGRAPHS MINING BASED ON SPANNING TREE ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.142-151, February-2019, Available :http://www.jetir.org/papers/JETIR1902B19.pdf

"PRIVATE MAXIMAL FREQUENT DIGRAPHS MINING BASED ON SPANNING TREE ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp142-151, February-2019, Available at : http://www.jetir.org/papers/JETIR1902B19.pdf